Visual representations of recurring revenue management system data and predictions

ABSTRACT

Visual representations of recurring revenue management system data and predictions are disclosed. A user interface is generated for an electronic display, the user interface including a graphical representation of one or more recurring revenue assets, the graphical representation representing each of the one or more recurring revenue assets according to one or more metrics that define each of the one or more recurring revenue assets. A predicted outcome for at least one of the one or more recurring revenue assets is predicted, the calculating being from a predictive model that is based on aggregate historical information collected from one or more data sources. A graphical representation of the predicted outcome is generated according to one or more parameters related to the one or more metrics, for concurrent display in the user interface with the graphical representation of one or more recurring revenue assets.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of application Ser. No. 13/895,302,filed May 15, 2013, which is a continuation-in-part of the followingco-pending patent applications: U.S. patent application Ser. No.13/841,681, filed Mar. 15, 2013, entitled “Asset Data Model ForRecurring Revenue Asset Management;” U.S. patent application Ser. No.13/844,306, filed Mar. 15, 2013, entitled “Predictive Model Of RecurringRevenue Opportunities;” U.S. patent application Ser. No. 13/844,002,filed Mar. 15, 2013, entitled “Recurring Revenue Asset Sales OpportunityGeneration;” U.S. patent application Ser. No. 13/842,035, filed Mar. 15,2013, entitled “Provenance Tracking And Quality Analysis For RevenueAsset Management Data;” and U.S. patent application Ser. No. 13/842,398,filed Mar. 15, 2013, entitled “Inbound And Outbound Data Handling ForRecurring Revenue Asset Management;” all five of which claim priorityunder 35 U.S.C. §119(e) to U.S. Provisional Patent Application Ser. No.61/661,299, filed Jun. 18, 2012 and entitled “Recurring Revenue AssetManagement System and Method.” This application is also related to thefollowing co-owned applications: U.S. patent application Ser. No.13/895,276 filed May 15, 2013, entitled “Multi-Tier Channel PartnerManagement For Recurring Revenue Sales;” and U.S. patent applicationSer. No. 13/895,294 filed May 15, 2013, entitled “Recurring RevenueManagement Benchmarking.” The disclosure of each document identified inthis paragraph is incorporated by reference herein in its entirety.

TECHNICAL FIELD

The subject matter described herein relates to data processing andmanagement, and more particularly to processing and managing recurringrevenue asset information, for example in support of sales execution andopportunity management for recurring revenue streams, and for generatinga visual representation of recurring revenue management system data andpredictions.

BACKGROUND

Innovations in products or marketing, the introduction of new orimproved products, sales to new customers or additional sales toexisting customers, and other approaches to expanding the amount ofmarket share controlled by a company or other commercial entity havehistorically been primary drivers for revenue growth. However, asmarkets mature and consolidate, a larger fraction of revenue realized bya commercial entity (used herein to refer broadly to a company,corporation, organization, person, group of persons, or the like thatundertakes commercial activity, including but not limited to sales ofproducts and/or services to customers) may be derived from renewals ofrecurring revenue assets. As used herein, a recurring revenue asset cangenerally refer to one or more of maintenance and/or support agreementsfor software or hardware, service covered products, service contracts,subscription agreements, warranties, renewables, or the like.

Many commercial entities fail to maximize realization of revenue fromrecurring revenue assets at least in part because management of suchassets is not well supported by currently available approaches.Currently available tools for managing recurring revenue are spreadacross customer relationship management (CRM) systems, partnerrelationship management (PRM) systems, data warehouses, entitlementsystems, billing systems, and even spreadsheets or the like. However,recurring revenue management presents a number of challenges that differin substantial and important ways from the typical focus of the salesstaff of a commercial entity.

A typical sales process involves closing a sale or deal and moving on toa next customer. In contrast, a recurring revenue relationship is bydefinition repetitive, and is not efficiently tracked or managed bytools designed to support initial sales. In addition, the commercialentity's existing investments in sales personnel, sales processes, andinformation systems are typically directed to, and are optimized andsupported with appropriate technology for, achieving maximum revenueperformance in the product or services sales business, not themanagement of recurring revenue streams and the sales activitiesnecessary to ensure that recurring revenue assets are maintained andeven increased in value. Optimizing the recurring revenue such asservice contracts and other service assets requires resolving challengesthat span multiple areas of a company's operations. In today'sever-changing market landscape, service revenues have become integrallyrelated to the success of a commercial entity. The problem, however, isthat as commercial entities begin to recognize the need for this updatedfocus, they run into a host of challenges for which they are notprepared.

For example, a company often has to deal with fragmented data fromdisparate sources, and this data can be inaccurate, incomplete andsometimes duplicated, particularly in a sales channel or when sellingthrough the channel. Further, in the case of a merger or acquisition,various legacy systems lead to complexity. Another challenge is a lackof specialization toward service contracts or service renewals. Mostcompany data is centered on the products it sells, rather than theservices contracted post sale of those products. A lack of industryfocus on recurring service revenue has lead to a dearth of standardmetrics and proven best practices.

Previously available approaches to recurring revenue management have notincluded a unified repository, or even any other way of collecting andorganizing these types of information that are important to optimizingrecurring revenue sales, that integrates data from multiple, disparateexternal client systems in support of an asset data model capable ofresolving the key questions of what (e.g. services, serviceableproducts, etc.) has been purchased, who (e.g. which commercial entity)made the purchase or purchases, who is the end user of the purchasedservices or serviceable products, where are the purchased services orserviceable products being used, how are the purchased services orserviceable products being used, how satisfied is the end user (andoptionally others at the commercial entity) with the purchased servicesor serviceable products, where are other opportunities for sales ofadditional related or unrelated services to the commercial entity as aresult of the sale of the purchased services or serviceable products,and the like. Furthermore, software systems to automate and optimize therecurring revenue market have generally been limited in regards toanalytics specific and relevant to recurring revenue assets,well-developed key performance indicators (KPIs) for management of suchassets, the ability to acquire and aggregate the data streams, and otheruseful features that support optimization of recurring revenue sales andrenewals.

Additionally, prior art approaches to recurring revenue asset managementhave generally lacked user interface features that provide the mostrelevant data or information to a user to assist in optimizing recurringrevenue asset sales.

SUMMARY

Consistent with one or more aspects of the current subject matter, arecurring revenue repository can be maintained with recurring revenueinformation obtained from one or more external data sources as wellsfrom ongoing sales activities (e.g. user interaction and updating of thedatabase). Some aspects of the current subject mater can include use ofa recurring revenue asset data model generated from at least a portionof this recurring revenue information. The recurring revenue asset datamodel can represent relationships among recurring revenue assets andorganizations, previously sold products underlying the recurring revenueassets, people and products associated with the recurring revenue assetsand the underlying previously sold products, and the like. A number ofanalytics and business intelligence processes can optionally be executedon the recurring revenue asset data model and on the recurring revenueinformation in the recurring revenue repository.

In some aspects of the current subject matter, a method includesgenerating a user interface for an electronic display. The userinterface includes a graphical representation of one or more recurringrevenue assets, the graphical representation representing each of theone or more recurring revenue assets according to one or more metricsthat define each of the one or more recurring revenue assets. The methodfurther includes calculating a predicted outcome for at least one of theone or more recurring revenue assets, the calculating being from apredictive model that is based on aggregate historical informationcollected from one or more data sources. The method further includesgenerating, for concurrent display in the user interface with thegraphical representation of one or more recurring revenue assets, agraphical representation of the predicted outcome according to one ormore parameters related to the one or more metrics.

Implementations of the current subject matter can include, but are notlimited to, systems and methods including one or more features asdescribed herein as well as articles that comprise a tangibly embodiedmachine-readable medium operable to cause one or more machines (e.g.,computers, etc.) to result in operations consistent with these features.Similarly, computer systems are also described that may include one ormore processors and one or more memories coupled to the one or moreprocessors. A memory, which can include a computer-readable storagemedium, may include, encode, store, or the like one or more programsthat cause one or more processors to perform one or more of theoperations described herein. Computer implemented methods consistentwith one or more implementations of the current subject matter can beimplemented by one or more data processors residing in a singlecomputing system or multiple computing systems. Such multiple computingsystems can be connected and can exchange data and/or commands or otherinstructions or the like via one or more connections, including but notlimited to a connection over a network (e.g. the Internet, a wirelesswide area network, a local area network, a wide area network, a wirednetwork, or the like), via a direct connection between one or more ofthe multiple computing systems, etc.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims. While certain features of the currently disclosed subject matterare described for illustrative purposes in relation to an enterpriseresource software system or other business software solution orarchitecture, it should be readily understood that such features are notintended to be limiting. The claims that follow this disclosure areintended to define the scope of the protected subject matter.

DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, show certain aspects of the subject matterdisclosed herein and, together with the description, help explain someof the principles associated with the disclosed implementations. In thedrawings,

FIG. 1 shows a diagram illustrating data flows in a recurring revenuemanagement framework consistent with implementations of the currentsubject matter;

FIG. 2 shows a diagram showing data exchanges between a recurringrevenue management system consistent with implementations of the currentsubject matter and examples of external client systems;

FIG. 3 shows a diagram illustrating features of an asset data modelconsistent with implementations of the current subject matter;

FIG. 4 shows a diagram illustrating relationships between components ofa recurring revenue management framework consistent with implementationsof the current subject matter;

FIG. 5 shows a process flow chart illustrating features of a methodconsistent with implementations of the current subject matter;

FIG. 6, FIG. 7, FIG. 8, and FIG. 9 show screenshot views of displays ofanalytical detail information consistent with implementations of thecurrent subject matter;

FIG. 10 shows a process flow chart illustrating features of a methodconsistent with implementations of the current subject matter;

FIG. 11 shows a diagram illustrating features of a sales processconsistent with implementations of the current subject matter;

FIG. 12 shows an annotated user interface view illustrating featuresrelating to bifurcation of sales team responsibilities;

FIG. 13, FIG. 14, FIG. 15, and FIG. 16 show screenshot views of focusedviews that can be displayed to sales representatives, sales operationsteam members, and managers, respectively, consistent withimplementations of the current subject matter;

FIG. 16, FIG. 17, FIG. 18, and FIG. 19 show screenshot views ofanalytics screens relating to different metrics or forecasts consistentwith implementations of the current subject matter; and

FIG. 20 shows an example of a computing landscape having one or morefeatures that can be included in implementations of the current subjectmatter.

When practical, similar reference numbers denote similar structures,features, or elements.

DETAILED DESCRIPTION

This document describes recurring revenue management systems andmethods, techniques, etc. relating to operations and functionality insupport of recurring revenue management. Consistent with implementationsof the current subject matter, a recurring revenue management system caninclude a recurring revenue assets repository that stores data relatingto recurring revenue assets, which are discussed in greater detailbelow. A unified data store model can be provided to manage bothtransactional and analytical uses of data. In some implementations ofthe current subject matter, the use of dynamically generated and updatedin-memory data structures (including but not limited to data cubes andthe like) can facilitate rich analytical exploration of data in nearreal-time, thereby allowing adaptation to customer-specific dimensionsand metrics. In some implementations of the current subject matter, ananalytics framework supports role-specific dashboards, and a plug-inmodel with rich representational state transfer (REST) applicationprogramming interfaces (APIs) can be included to support use ofthird-party ad hoc analytics products. Also consistent withimplementations of the current subject matter, data maintained in therecurring revenue management system can be organized according to anasset data model that includes data objects and defined or derivedrelationships between these data objects. The data objects can berepresentative of products sold by a commercial entity; recurringrevenue assets of that commercial entity; opportunities for sale orrenewal of new or existing recurring revenue assets; and contactsrelated to one or more of the products, recurring revenue assets,opportunities, and other contacts.

FIG. 1 shows a diagram 100 showing data flows occurring in a recurringrevenue management system consistent with implementations of the currentsubject matter. As shown in FIG. 1, multiple data sources can contributedata that are included within an asset data model, which can includerecurring revenue assets information in a recurring revenue assetsrepository 102, opportunity and/or offer information in an opportunitiesrepository 104, quotation information in a quotations repository 106,bookings information in a bookings repository 110, contacts informationin a contacts repository 112, and the like. It should be noted thatwhile various repositories in FIG. 1 are depicted as physically separatefrom one another, any data storage configuration and arrangement of oneor more repositories is within the scope of the current subject matter.For example, all or some of the repositories 102, 104, 106, 110, 112 inFIG. 1 can be included within a single repository structure, which canbe hosted at a single storage facility or alternatively in distributedstorage (e.g. in the “cloud”).

Referring again to FIG. 1, the recurring revenue assets repository 102can include information about instances of covered assets (e.g. productssuch as physical items or software licenses, etc. that can be sold by acommercial entity to one or more customers of the commercial entity,which are also referred to herein as covered assets) that have beensold. These covered assets form the basis for recurring revenue assetsbecause they constitute an existing instance of a seller-purchaserrelationship between the commercial entity and the customer whopurchased the covered assets. The recurring revenue assets (serviceassets) themselves can include, among other assets, service contracts,warranties, maintenance agreements, and the like sold by the commercialentity to the customer in association with or to provide an ongoingservice arrangement for the covered assets.

Because it relates to an existing and ongoing relationship with thecustomer, the information about products and their associated recurringrevenue assets that is contained in the recurring revenue assetsrepository 102 can be used to generate one or more opportunities forrenewal of an existing recurring revenue asset (to thereby maintain therecurring revenue stream) and in some cases to increase the value of therecurring revenue asset (for example by selling a higher valueagreement, service contract, etc. to the customer). As used herein,opportunities are groupings of offers targeted for execution of a saleto a customer, and offers are individual instances of a recurringrevenue asset available for renewal. The groupings of offers in anopportunity can include multiple offer instances for the same customer.In some examples, the offers grouped into an opportunity can all haveexpiration dates during a sale period (e.g. a month, a quarter, a year,etc.). Opportunities can be collected in the opportunity repository 104.

Once acted upon by sales staff who make contact with appropriatedecision makers at the customer, opportunities can be moved into aquotation stage during which quotations related to renewals, increases,etc. of existing recurring revenue assets can be generated. Quotations,which include a transaction offer for a set of offers included in anopportunity, can be stored in the quotation repository 106. As aquotation is transitioned to a deal being closed, a booking, whichrefers to a set of offers accepted by a customer, can be generated andstored in a bookings repository 110. Resolution of a booking results ingeneration of a new recurring revenue asset, which can be retained inthe recurring revenue asset repository 102 for use in generating futureoffers and opportunities. For example, when a resolved service contractor the like approaches its next expiration, a new offer for renewal ofthat service contract can be generated and optionally grouped with otheroffers for the same customer with similarly timed expiration dates intoa new opportunity. Additional data that impact the generation ofopportunities and execution of those opportunities includes contactinformation for the decision makers and other important contacts (e.g.individuals and companies involved in the sales process) with whom salesstaff need to communicate. This contact information can be stored in thecontacts repository 112.

Efficient and structured assembly of the information contained in therepositories 102, 104, 106, 110, 112 depicted in FIG. 1 can be animportant aspect of a recurring revenue management approach consistentwith implementations of the current subject matter. However, much ofthis information is typically contained (or at least originates) inother data management systems (referred to herein as external clientsystems 114) than the recurring revenue management system, as discussedin greater detail below in reference to FIG. 2. The term “client” isused herein to refer to a commercial entity that employs features orfunctions of a recurring revenue management system provided or supportedby a recurring revenue management vendor or service provider.Effectively, as used herein, the client is a commercial entity that is acustomer of the recurring revenue management vendor or service provider.The client commercial entity in turn has customers to whom the clienthas sold and/or wishes to sell or renew recurring revenue assets such asthose described elsewhere herein. A customer of a client commercialentity typically has an installed base of products, for which thecustomer can purchase various service agreements, contracts, etc. Ineffect, the products in the installed base of products can be consideredas covered assets for which the commercial entity can sell to thecustomer one or more service contracts, agreements, etc. From theperspective of the client commercial entity, these service contracts,agreements, etc. can be considered to be service assets or recurringrevenue assets.

A recurring revenue management approach consistent with implementationsof the current subject matter can be configurable for the storage,retrieval, searching, and analysis of a wide range of recurring revenueinformation. Data relating to service assets or other recurring revenueassets, referred to herein as “recurring revenue data,” can includeinformation pertaining to what a commercial entity has sold, such as forexample services, products for which related services can be sold, etc.(e.g. the covered assets). Recurring revenue data can also includeinformation about the purchasers, users, locations of use, modes of use,etc. of such services, serviceable products, etc. In other words, therecurring revenue data can include information about what recurringrevenue assets an entity has, who the customers and users of therecurring revenue assets are, and where and how the recurring revenueassets are being used. Recurring revenue data can further includemetrics relating to satisfaction of the customer or user of therecurring revenue asset, as well as information relating to potentialopportunities for services contract sales (e.g. for serviceable productssoon to come off warranty, etc.), and the like.

The recurring revenue management system can import diverse information,and of various formats, from external client systems 114 into a commonasset data model. The asset data model can facilitate a wide range ofrecurring revenue management capabilities, and can provide automatedassistance for entity resolution (resolving external entities topre-existing entities in the system). The recurring revenue managementsystem can also optionally maintain provenance information for allinformation loaded into the system (e.g. all recurring revenue data fromeach of the one or more data sources) to be able to discern informationorigin.

As noted above in reference to FIG. 1, a recurring revenue managementsystem consistent with implementations of the current subject matter canreceive and assign a usable structure to information obtained from avariety of external clients systems 114. FIG. 2 shows a diagram 200illustrating potential sources of data or other information that can beacquired by or received at a recurring revenue management system 202.Among other possible data sources, the external client systems 114referenced in FIG. 1 can include one or more of an entitlement system204, an order management system 206, a pricing system 210, a productmaster database 212, a sales force automation system 214, a customermaster database 216, a contract management system 220, user adoption andusage data sources 222, a customer service database 224, and the like.It will be understood that the individual external client systems shownin FIG. 2 can, in some implementations of the current subject matter, beprovided by one or more systems that support features of more than onesuch external client system 114. For example, external client systemscan include customer relationship management (CRM) systems, enterpriseresource planning (ERP) systems, business intelligence (BI) systems,calendar and contact systems or databases, customer service systems,inventory systems, accounts receivable systems, sources of customerusage and engagement information, and the like. In general, a commercialentity acquires these various systems, databases, and the like from morethan one different software or computing system provider, each of whichtypically uses distinct or proprietary database structures, dataformats, report configurations, application programming interfaces(APIs), and the like that are optimized for the specific tasks to beperformed by its product. Assembly of information from a group of datasources with multiple, potentially disparate database structures anddata formats can present a challenge for any system that seeks tocollect such information, present it in a usable format, supportdetailed analytics based on the data, etc. Further challenges can bepresented by the need to authenticate potentially conflictinginformation received from more than one source. Additionally,information collected in a recurring revenue management systemconsistent with implementations of the current subject matter istypically not static. Rather, users interacting with the system arelikely to update or correct the information based on communications withcontacts, etc. Execution of tasks depicted in FIG. 1 can also alter the“correct” version of some of the information received into the recurringrevenue management system.

An approach to this problem typically involves receipt of the data atthe recipient system from a source system, creation of customizedmappings (or application of pre-created mappings) between datastructures of each source system and the data structures of therecipient system, and transformation of the data from the source systemdata structures to the recipient system data structures according to themappings. Subsequent to import of data from the data sources but priorto making the imported data available for productive use by end users atthe recipient system, one or more quality control operations generallyoccur to ensure consistency of the imported data; to identify duplicateand potentially conflicting records; and to alert one or more users withappropriate permissions levels to the presence of any errors, partialimports, missing data, or the like. For export of data from therecipient system back to the source system, a similar process can beundertaken, with the mapping being performed in reverse.

For an active recurring revenue management system, particularly onesupporting recurring revenue asset management for a large commercialentity with a sizable sales staff, a large number of recurring revenueassets, and a complicated information landscape in which multiplesystems maintain data records relevant to support of recurring revenuemanagement activities, a quality control process that interruptsproductive use of the system subsequent to a data import is notdesirable. For example, in a large enterprise with multiple datastreams, quality control could be quite time consuming. Additionally, inthe interest of having the most current information available for use bythe recurring revenue management system, frequent data imports can beadvantageous. However, if each such import were to require substantialdowntime for the import and transformation processes to complete andquality assurance routines to execute, usability of features supportedby the recurring revenue management system would be negatively impacted.

A recurring revenue management approach consistent with implementationsof the current subject matter can include synchronization andintegration of data with a variety of disparate external client systems.For example a recurring revenue management system can access datarecords and formatted data from systems such as entitlement systems,order management systems, customer master systems, product mastersystems, and service delivery systems. The data from these externalclient systems is usually not static. Accordingly, it is advantageousfor the recurring revenue management system to be able to receive datafeeds from such systems at a high throughput rate. Furthermore, thereceived data is usable either upon receipt at the recurring revenuemanagement system or at least within a very short processing time.

FIG. 3 illustrates features of a asset data model 300 that can includeinterrelated data objects 301 that support the recurring revenuemanagement features and functions described elsewhere herein. Therecurring revenue management system 302 can support an asset data model300 as a flexible mechanism for representing complex relationships amongassets, organizations, people, and products, and on which a number ofanalytics and business intelligence processes can be executed. Therecurring revenue management system can provide one or more applicationprogramming interfaces (APIs) or other standardized interface to accessservice renewal assets in the recurring revenue repository, and therecurring revenue repository information model used by the repositoryprovides a uniform means of analyzing, searching, and manipulatingrecurring revenue data.

Consistent with implementations of the current subject matter, arecurring revenue management system 202 and an included asset data model300 can include product data objects 302, recurring revenue asset dataobjects 304, opportunity data objects 306, and contact data objects 310.Product data objects 302 represent products (e.g. covered assets), whichas noted above in reference to FIG. 1, can include items sold tocustomers and can be considered as part of the customer's installed baseof covered assets. A framework of data objects 301 can include a set ofcommon relationships related to recurring revenue management, theserelationships can include product relationships 312, asset relationships314, opportunity relationships 316, and contact relationships 320.Products can provide the basis for a recurring revenue assetsrepresented by instances of asset data objects 304 via one or moreproduct relationships 312 between a product represented by a productdata object 302 and the instances of an asset data object 304representing the recurring revenue asset or assets. Productrelationships 312 can also optionally exist between product representedby a product data object 302 and one or more of recurring revenue assetsrepresented by instances of asset data objects 304, opportunitiesrepresented by instances of opportunity data objects 306, contactsrepresented by instances of contact objects 310, other productsrepresented by other instances of a product data object 302, etc.

Further with reference to FIG. 3, asset relationships 314 can existbetween a recurring revenue asset represented by an instance of an assetdata object 304 and one or more of a product represented by an instanceof a product data object 302, a contact represented by an instance of acontact data object 310 (e.g. a person, organization, etc. relevant to arecurring revenue asset), an opportunity represented by an instance ofan opportunity data object 306 (e.g. a bundle of offers relating torenewal of existing recurring revenue streams and/or to establishment ofnew or increased value recurring revenue streams), another recurringrevenue asset represented by another instance of an asset data object304, etc.

Opportunity relationships can exist between an opportunity representedby an instance of an opportunity data object 306 and one or more of aspecific recurring revenue asset represented by an instance of an assetdata objects 304, a product represented by an instance of a product dataobject 302 underlying the recurring revenue asset(s), a contactrepresented by another instance of a contact data objects 310, anotheropportunity represented an instance of an opportunity data object 306,etc. Contact relationships can exist between a contact represented by aninstance of a contact data object 310 and one or more of a recurringrevenue asset represented by an instance of an asset data object 304(for example based on communications relating to an initial sale of arecurring revenue asset 304 and/or to one or more renewals of therecurring revenue asset 304), a product represented by an instance of aproduct data object 302 underlying the recurring revenue asset(s), anopportunity represented by an instance of an opportunity data object306, another contact represented by another instance of a contact dataobject 310, etc.

The data objects 108 discussed above can optionally also be extendible,for example through customer-specific extensions fields. Additionalcustom objects can be added to a customer's configuration. In addition,the set of base relationships can be extended to match specificrequirements of the commercial entity using the recurring revenuemanagement system 202. Relationships between an instance of the productdata object 302, the asset data object 304, the opportunity data object,and the contact data object can be established based on one or more ofthe data received from the incoming data streams, existing data in therecurring revenue management system 202, user input, and the like. Theinstances of the data objects 301 and the defined relationships betweenthese instances together make up the asset data model 300.

An asset data model 300 such as that described herein can provide auseful vehicle for exploring and understanding relationships that existamong product objects, asset objects, organization objects, contactobjects, and opportunity objects. Many of these relationships aredirectly derived from information that exists in source client systems,while others require inference on the part of the system, others aregenerated by the system as part of object generation, others are createdas a side effect of system actions, and still others are constructed asa result of actions by system users. In an illustrative but not limitingexample of an inference approach to generation of the relationshipsbetween objects, a relationship inference includes exploitation oftransitivity (e.g. if a relates to b, and b relates to c, a relationshipcan be instantiated between a to c), and an automated instantiation ofinverse relationships (if a relates to b, then there exists an inverserelationship from b to a).

A consistent approach to managing opportunities, quotes, and bookingsactivities can be provided through one or more features supported by anasset data model 300 consistent with implementations of the currentsubject matter. Such features can include, but are not limited to,detailed reports, pipeline dashboards, opportunity details, territorymanagement, and the like. Detailed reports allow sales representativesto proactively identify and address at-risk opportunities, whilepipeline dashboards allow managers to view pacing of sales activities bytime period and production metrics. Opportunity details can allowviewing of deal progress and logged activities by sales representative,sales operations assistants, managers, and other staff. Territorymanagement allows managers to manage and drive sales representatives'behavior by assigning tasks to opportunities based on existingpriorities. Territory can refer to divisions of available opportunitiesbased on one or more factors, which can include but are not limited togeography, customer groupings, subject matter, etc.

Through the asset data model 300, a clearer view can be presented tousers (e.g. sales staff, sales support staff, managers, executives,etc.) of the installed base of products, services, etc. that have beenpreviously sold to customers of the commercial entity and that providethe foundation for new and renewal sales of recurring revenue assets. Asdiscussed elsewhere herein, the clearer view can be presented to usersvia one or more user interface displays, computer-generated interfaces,or the like. Management of this installed base using an asset data model300 can improve accuracy and insight into the customer base and enableleveraging of this installed base data to maximize recurring revenuestreams. Increased sales can be achieved through improved quoteaccuracy, consolidation, identification of cross-sell and up-sellopportunities, better handling of renewal contacts and processes, andthe like. Service delivery efficiency and customer satisfaction can beincreased through improved entitlement, shipping, and staffing resourceplanning. Prediction of customer behavior can also improve, which canfacilitate optimization of sales staff activities.

Among other information that can be received in the inbound data streamsand incorporated into appropriate instances of data objects 301 in theasset data model 300, the asset data model can store and organizeexpiration information for existing recurring revenue assets, productinformation (e.g. characterizations of the install base including who,what, where, etc. information), relationships (e.g. predecessors,successors, associated opportunities, covered products, customers,partners, locations, etc.), expected value analyses (e.g. projectedfuture revenue, product margin analyses, etc.), and the like.

A recurring revenue management system including one or more features asdescribed herein can provide a scenario-based revenue projection fromthe recurring revenue repository of service asset data and otherrecurring revenue data. In some implementations, the recurring revenuemanagement system generates a user interface to display a projection ofrevenue over time from a set of assets based on expiration dates,product information, projected sales timing, and projected saleseffectiveness.

A recurring revenue management system including one or more features asdescribed herein can also receive input from a user to define patternsthat, when applied to data of the recurring revenue repository, yieldone or more sets of opportunities generated for sales execution. Logicof the recurring revenue management system includes an application thatexecutes one or more methodologies to identify different classes ofopportunities (e.g. renewal, cross-sell, up-sell, etc.) as well asoptimizing offer grouping within opportunities to improve salesproductivity. The recurring revenue management system can encode andautomate computer-based processing for automated guidance of team-basedselling of services (recurring revenue sales).

In various aspects consistent with one or more implementations of thecurrent subject matter, recurring revenue sales execution can besupported by one or more of an opportunity generation and handlingapproach, forecasting and targeting approach, a sales-stage managementapproach, an integrated sales operations approach, a channel partnermanagement approach, and sales automation approaches. In other aspects,one or more sales operations functions, such as for example quoteprocessing, bookings processing, and data services, can also beprovided. Various operations in support of such features can optionallybe based at least in part on use of an asset data model 300 of arecurring revenue management system 202 as discussed above. It will beunderstood that while the approaches described herein make reference tovarious features and functions of an asset data model 300, the use ofsuch a model is not mandatory unless otherwise so stated. The followingdescription and the accompanying figures relate to various advantageoususe features related to these aspects as well as technical features viawhich such use features can be provided.

FIG. 4 shows a diagram 400 illustrating components of a recurringrevenue management approach that can be based on one or more features ofa recurring revenue management system 202 and asset data model 300 suchas those discussed above. The diagram 400 shows interaction of thevarious components within the context of a sales process for a serviceasset 402. The service asset 402 is related to a covered asset 404 thathas been previously sold, leased, etc. to a customer by a commercialentity (in this example, the commercial entity is the user of arecurring revenue management system 202). An existing service asset 402can have an expiration date. As that expiration data approaches, arecurring revenue management system 202 can generate an offer 406 basedat least in part on data stored and organized in an asset data model 300that defines relationships between data objects 301, for example dataobjects relating to products (e.g. covered assets 404), opportunities,contacts, service assets, etc. The offer 406 can optionally be one of agroup of two or more offers that are grouped into an opportunity basedon proximity of the expiration dates of two or more service assetsrelating to a same customer. The grouping of offers into opportunitiescan be performed intelligently based on the asset data model 300 and therelationships defined therein, for example, such that an opportunitygroups offers that should be directed to a common decision maker at asingle customer. The offer, optionally as part of an opportunity, can beinitially classified as “not contacted” 410 during a qualification phaseof opportunity processing. As a sales representative makes initialcontact with a buyer or decision maker at the customer, the opportunitycan be reclassified as “contacted.”

Upon a sales representative making a successful contact with a buyer orother decision-maker at the customer, and upon identifying a desire onthe part of the buyer or decision maker to explore renewing of theservice asset 402, the sales representative 412 can request a quote 414and this can be logged into the recurring revenue management system 202.A quotation record 416 can be generated and forwarded to a salesoperations team member 420. When the sales operation team member 420completes the quotation (and optionally logs this into the recurringrevenue management system 202), the recurring revenue management system202 can notify the sales representative 412 that the generated quote canbe sent to the buyer. In optional variations, the recurring revenuemanagement system 202 can automatically detect when a quotation has beencompleted, and can provide the notification to the sales representative412 that this has occurred.

The sales representative 420 can deliver the quotation to the buyer orother decision maker 422, and this delivery event can also be recordedby the recurring revenue management system 202 as further tracking ofthe sales process. When the sales representative 412 logs into therecurring revenue management system 202 that a purchase order has beenreceived, the sales process state can be updated accordingly 424. Therecurring revenue management system 202 then generates a booking record426. When the sales operation team member 420 records that the sale hasbeen completed 430, the sales phase will move to closed sale. A nextgeneration service asset can be generated, for example with anotherinstance of an opportunity object 306 that has a relationship to theinstance of the product object associated with the covered asset 404.This next generation service asset provides a starting point for a nextoffer to renew the service asset 402 as the next generation serviceasset approaches its expiration date.

The asset data model 300 can also support real-time (or at least nearlyor approximately real-time) reporting and interpretation of serviceassets, installed base, total asset value, projected revenue, and dataquality as well as subscriptions planning (e.g. evaluating,categorizing, and prioritizing subscriptions) to drive sales programs,opportunity generation (e.g. creating opportunities based on installedbase or subscription planning outcomes. In this manner, the asset datamodel 300 can be used in generation of offers and opportunities, forexample based on inter-data object relationships and information thatcan be included in or derived from the asset data model 300. Thisadditional information can include, but is not limited to exclusionrules; expiration window filters; product, customer, or region filters;target selling periods, upsell and cross-sell opportunities, upgraderequirements, expected outcome analysis, and the like.

An approach to opportunity generation and handling consistent withimplementations of the current subject matter can be better understoodin reference to FIG. 5. At any particular time, a user or an automatedprocess can select a filtered set of service assets to be used as asource population for opportunity generation. Typically this filter caninclude a range for the expiration dates of the service contracts thatcorresponds to a targeted selling period (e.g. a time period in whichnew contract or renewal sales are expected to close). The expirationdate range can typically include dates corresponding to one ofexpiration dates before the selling period (expired or carryovercontracts), expiration dates during the selling period (in period), andexpiration dates after the selling period (futures). Opportunitygeneration can be an automated process that operates on a selected setof service assets. An example of an opportunity generation approach canbe better understood by reference to the process flow chart 500, whichillustrates features of a method consistent with one or moreimplementations of the current subject matter.

At 502, the filtered set of service assets discussed above is selected.In various implementations of the current subject matter, the selectingof the filtered set of service assets can occur automatically, forexample via a computing system applying one of the filters discussedabove to an asset data model that includes a collection of data objectsincluding asset data objects associated with service assets providingrecurring revenue streams to a commercial entity. At 504, a new serviceoffer based on one or more specific rules defined for the commercialentity is created for service assets in the selected set. The creatingcan optionally include applying one or more offer creation parameters. Anon-limiting example of an offer creation parameter includes a type ofrenewal to be offered (e.g. a “like for like” service, which can also bereferred to as a renewal par, an upgrade in service, etc.). The creatingcan also include looking up or otherwise determining an appropriaterenewal stock-keeping unit (SKU) or other reference information for anexpiring asset and a chosen service level, looking up or otherwisedetermining a price for new a new SKU or other reference information fora renewal of the expiring asset, copying relevant information from theexpiring service asset (e.g. contact information, etc.), and the like.The relevant information can in some implementations be stored in anasset data object associated with the expiring service asset or in acontact data object having a relationship with the asset data objectassociated with the expiring service asset, or the like. The copying caninclude transferring the data from an asset data object associated withthe expiring service asset to a new asset data object associated withthe new service asset generated by a renewal, establishing arelationship between a contact data object having a relationship withthe new asset data object associated with the new service assetgenerated by the renewal, or the like.

At 506, the set of created offers can optionally be grouped into two ormore smaller sets of opportunities. The grouping of offers into smallersets of opportunities can be performed to optimize sales efficiency. Thegrouping can in some examples involve associating offers for a sametarget customer, a same reseller organization, or the like. Inadditional or alternative variations, grouping can be based on otherconsiderations, such as for example a same or related product type, etc.At 510, the offers, either as sets of opportunities or individually, canbe assigned to one or more sales teams. The assigning can be based ondata contained within the opportunity, for example in an opportunitydata object associated with the opportunity. In further implementationsof the current subject matter, opportunity generation can optionallyinclude more complex logic to automatically add appropriate add-onservices to the offers or to create offers corresponding to cross-sellopportunities.

Implementations of a recurring revenue management system include userinterface features, implemented in an electronic graphical display, thatprovide the most relevant data or information to a user to assist inoptimizing recurring revenue asset sales. In one example, the userinterface can include a graphical feature that highlights or otherwisedraws a user's attention to one or more opportunities, groups ofopportunities, values of one or more metrics, or the like, based onpredictive analysis. The user interface features provide real-timeanalytics, comparative benchmarking to management-generated goals or topredictive outcomes, and customizable forecasting based on the dynamicselection of one or more metrics or definition of one or more parametersrelated to a recurring revenue asset. The user interface generated bythe recurring revenue management system can be customized for any typeof electronic display device or dimensions and capabilities thereof.

In some implementations, a recurring revenue management system isconfigured for generating graphical representations of individualrecurring revenue assets based on data attributes associated with thoseassets. The system is further configured for generating graphicalrepresentations of sets of recurring revenue assets that can be filteredor ordered based on data attributes associated with those assets. Insome implementations, a recurring revenue management system isconfigured for generating graphical representations of information aboutsets of recurring revenue assets where—the sets can be defined byfilters on asset data attributes—and the information is defined by oneor more metrics that are defined by aggregate functions on attributes ofrecurring revenue assets in the set, and one or more attributes used asdimensions for metric grouping. The system can calculate predictivemetrics or dimensions for recurring revenue assets based on aggregatehistorical information collected from one or more data sources.

In one implementation, renewal sales opportunities can be displayedconcurrently with one or more predictions generated from one or morepredictive models. Each of the one or more predictive models can bebased on any number of metrics, and can function with additionalcapabilities beyond a simple ad hoc query tool. For example, intelligentsorting of the opportunities presented in the list of opportunities canhighlight probable outcomes, and the like. The metrics that aredisplayed concurrently with the list of opportunities can be dynamicallyselected and changed for display in the user interface, to support theability of a user to gain rapid insight into the sales activities orchanges in sales activities that may be most useful in improving salesperformance.

Implementations of the current subject matter can display a number ofadditional data sources or results derived from data sources that arehighly relevant to recurring revenue asset sales success. For example a“days in advance” (DIA) metric, historically based metrics, and othermetrics, etc. can be displayed in combination with sales/renewalopportunities. The DIA metric is related to a time period beforeexpiration of a currently active service asset. The time period ispreferably expressed in terms of days, but may also be expressed interms of other time intervals such as weeks, months, quarters or anyother time periods. The DIA metric can be used in predictive models toestimate an optimal timing relative to expiration of an existing serviceasset, to determine a time period in which contact with a customer ismost likely to yield success in the recurring revenue asset sale.

In some implementations, a user interface can display information thatboth characterizes one or more sales opportunities, and characterizesone or more metrics that can be used in predicting the likelihood of afavorable outcome of any of the one or more sales opportunities. Forexample, the user interface can display a list that includes multiple(two or more) opportunities that are presented with rankingcapabilities. The ranking capabilities can include rankings based oncriteria such as distance from the expiration date or the like.

Additional features of implementations of the current subject matterinclude the ability to query and/or sort a list of opportunities basedon one or more metrics that are used in a predictive model. For example,these metrics can include a length of time from first contact, a lengthof time for first quoting of an opportunity relative to a close date, anumber of revised quotes delivered to a customer before closing, ameantime to complete a quote request, and the like.

A predictive model can also include the ability to diagnose problemsthat a sales organization, an individual salesperson, or any otherindividual or group of individuals may be experiencing that lead to lessthan optimal sales performance. A desired output of a predictive modelcan include a probability of a successful close of a renewal sales offerat any given time and or at any given current value compared with one ormore metrics used in the predictive model.

Some implementations of the current subject matter can supportunderstanding the distribution of expiring assets over time, for exampleas part of long range planning of potential future revenue. Thisfunctionality need not involve creation of opportunity objects. FIG. 6shows an example screenshot view 600 illustrating a display of variousanalytics information that can be presented to a user based oninformation organized in an asset data model 300. The data displayed canoptionally be dynamically updated to reflect an at least near real timeview on the status of the install base 602, data quality 604, dataimport statistics 606, value of the recurring revenue asset stream (e.g.both assuming renewal of contracts at their existing value 610 and withsome amount of upsell 612, projected service asset revenue (e.g. withprojections for a benchmark amount 614, a historic trend 616, and a“what-if” or hypothetical prediction based on one or moreuser-configurable parameters 620). Dynamic data updating can be providedin some implementations of the current subject matter via ahigh-performance, in-memory database backend.

Consistent with implementations of the current subject matter, acombination top down, bottom up approach to predictions of recurringrevenue from service assets can be supported by one or more of on-goingavailability of large streams of inbound data received from externalclient system 114; the ability to track, project, and analyze thesedata, and the like. In this approach, management personnel can set salestargets for a sales period (e.g. a month, a quarter, etc.), and salesoperations team members and/or sales representatives (explained in moredetail below) can generate updated forecasts, for example on a shortertimescale than the sales period (e.g. a day, a week, etc.). The term“target” as used herein can be considered as a business objectivecreated via a top-down approach, such as a desired goal set by amanager, supervisor, division vice president, etc. for a given salesperiod. In contrast, a “forecast” as used herein can be aforward-looking projection created by a sales employee to indicate wherethat sales person expects his or her sales numbers to be by the end ofthe sales period. FIG. 7 shows a chart 700 illustrating the relationshipbetween targets, forecasts, and “reality” as represented by currentclosed sales for a given sales period. In the example of FIG. 7, atarget line 702 represents a “top-down” projection or expectation set byone or more managers or management/executive staff while a forecast linerepresents a “bottom-up” estimate generated by input from actual salesstaff. The “current closed sales” line 706 represents actual salesresults updated (at least approximately) to the current date and/or time710. Comparison of these various metrics at various times, such as forexample as the sales period progresses, at the end of the sales period,etc., can be useful in a number of analytical measures relating to salesperformance. Examples of analytical measures that can be supported basedon data collected, generated, etc. using approaches consistent with thecurrent subject matter can include comparisons of sales performanceversus one or more predefined metrics, benchmarking against dataregarding other recurring revenue sales activities, etc. As explained infurther detail below, the benchmarking can occur against internal datafor a commercial entity, for example using the commercial entity'shistorical data as collected in an asset data model, etc. Alternativelyor in addition, external benchmarking is also within the scope of thecurrent subject matter.

Another feature of a recurring revenue management system consistent withimplementations of the current subject matter can involve support oftimely and accurate analysis of how a sales history can drive futuresales performance. Tools to assist with this effort can includedashboards and visualizations of analytical content such as graphs andcharts that display immediately upon login; a rich query interface thatcan support set operations, aggregations, background processing, andmaterialized collections; either or both of standard and client-specificreports; and externally callable REST APIs that facilitate the use ofthird party data exploration and reporting tools. FIG. 8 shows ascreenshot view 800 that illustrates an example of real time performancestatistics reported by analytical tools embedded in a dashboard such asdescribed herein. The dashboard shown in the view 800 can an overviewtab or page as well as various tabs or pages linked from the overviewthat enable a deeper dive into the collected sales data to allow a userof the system to access data pertaining to one or more metrics or viewon the sales data based on a user role. For example, a manager's view onthe data, metrics, etc. can differ from that of a sales representativeor sales operation team member. These details are discussed in greaterdetail below.

The available opportunity for a given sales period is generally definedas all opportunities expiring in the current sales period and anyremaining unresolved opportunities from the previous sales period.Another aspect of recurring revenue management that can be supportedusing approaches consistent with the current subject matter involvesdynamic calculation and real time or near real time updating of keyperformance indicator (KPI) metrics relating to sales performance andoptimization of a recurring revenue stream. Among other questions that asales team manager, other executive, etc. at a commercial entity maydesire to have answered are how successful the sales team is at coveringthe customer base, how successful the sales team is at convertingopportunities to a win, how successful the team is at upselling andcross selling, etc. Upselling generally involves closing an opportunityat an increase revenue point relative to the contract or other recurringrevenue stream that was expiring. This can include selling a higherlevel of service, a maintenance or insurance plan with a lowerdeductible, etc.

FIG. 9 shows a diagram 900 illustrating aspects of key performanceindicator calculations that can be used consistent with implementationsof the current subject matter. An existing recurring revenue stream canbe protected and, advantageously, enhanced by upselling addition of newrecurring revenue assets sold to an existing customer, etc. A mappingcan be developed to anticipated and observed customer buying behaviorsuch that a measurable “bucket” or aggregation of available renewal,upselling, and possible new sale opportunities can be collected tofacilitate forecasting and measuring of sales team performance.

Information that feeds KPI calculations can include a resolution rate,which measures the fraction of opportunities that were moved to finalstatus within the transaction period (e.g. closed sale+no service)divided by the available opportunity for a sales period; a close rate,which is the ratio of closed (successful) sales to total amount ofresolved opportunity (e.g. wins+losses), and measures the fraction ofopportunities resolved that were resolved as a win; and a conversionrate, which is an amount of “won” service asset value divided by atargeted, and therefore measures the actual transaction value of closedsale against target value.

The top down approach mentioned above can generally rely upon KPIs andother historical performance metrics applied to opportunity. Forexample, an available opportunity multiplied by a resolution ratemultiplied by a close rate multiplied by a conversion rate can yield aforecast for a sales period. As an example, a sales period with $1.2M inavailable opportunity, an 80% expected resolution rate, an 80% closerate, and a 105% conversion rate would have a forecasted target value of$806,000 (e.g. 1.2M times 80% times 80% times 105%=$806,000).

A bottoms up approach consistent with implementations of the currentsubject matter can make use of a “commit level” (red, yellow, green)approach, which is a specific forecast percentage applied to each deal.In this manner, a sales team members (representative or operationsdepending on the configuration of the sales team) can enter a “peropportunity” estimate of the likelihood of that opportunity beingsuccessfully resolved (a win). A simplified commit designation scheme(e.g. red/yellow/green) with a limited number of categories eachindicating some defined likelihood of a successful close can berelatively straightforward and not as time consuming such that a salesmember is more likely to take the care necessary to enter his or herpredictions on a regular schedule (e.g. once per week, etc.). Continuingwith the boom up approach, every target opportunity can be assigned acommit level representative of an expected outcome probability. Anexpected close date for each opportunity can also be entered orestimated. A weighted value of the opportunity can be the totalavailable opportunity value for that opportunity multiplied by thecommit level (percent chance of success). A forecast of total revenuefor a sales period can be the sum of the weighted values of theopportunities projected to close in that sales period.

Implementations of the current subject matter can also supportunderstanding the distribution of associated expiration dates for agiven set of offers or opportunities within a target selling period.Offers within a selling period can be differentiated by the expirationdate of the service asset associated with each offer. This expirationdate, in conjunction with historical comparison data, can be used tohelp predict the probability of that offer resolving and the probabilityof that offer closing within more specific date ranges in the sellingperiod (for example within a specific month in a quarter). A recurringrevenue management system 202 can support the ability to consistentlyconstrain predictions of outcome so that predictions of outcome early inthe selling period properly constrain predictions later in the sellingperiod. These constraints can be updated as time progresses in a quarterand as actual outcomes become available. Opportunity generation caninclude batching of existing and anticipated recurring revenue assetsaccording to their expiration dates. In some examples, a sales periodopportunity metric can be generated based on a calculation of the valueof renewal contracts that are available to be completed during the salesperiod. A sales period can be a different period of time than a quarter(e.g. a month, a week, a year or longer, a day or less, etc.).

An example of an predictive modeling can be better understood byreference to the process flow chart 1000 shown in FIG. 10, whichillustrates features of a method consistent with one or moreimplementations of the current subject matter. At 1002, a set of offersfor renewal of service assets is differentiated within a sales period.The differentiating can be based based at least in part on an expirationdate of a service asset associated with each offer of the set of offers.At 1004, a set of parameters representative of each offer in the set ofoffers is analyzed. The analyzing can include applying a predictivemodel based on outcomes of other offers for renewal of service assets.The set of parameters includes at least one user-provided constraint onan expected outcome of each offer. For example, the set of parameterscan include a predicted commit level assigned to each offer by arepresentative responsible for each offer. Commit levels are discussedin more detail below. At 1006, a predicted outcome for the set of offersis calculated based on an applied predictive model using the set ofparameters as inputs. The at least one constraint for at least one offerof the set of offers is updated based on at least one sales outcomeoccurring during the sales period at 1010, and at 1012, a predictedoutcome for the set of offers is recalculated based on the set ofparameters comprising the updated at least one constraint.

Implementations of the current subject matter can provide tools thatassist managers and sales staff at a commercial entity in tailoringtheir sales approach to the purchasing behavior of the customers of thecommercial entity. Such behaviors can impact the success of one or moreopportunities. Features consistent with the current subject matter canleverage features and capabilities of a recurring revenue managementsystem 202 and asset data model 300 as described herein to trackpotential opportunities, forecast sales results, target sales staffeffort and attention where and when it is likely to be most beneficial,and the like.

In general, an opportunity that is based on an expiring customercontract, other service arrangement, etc. can be assigned one or moredifferent status or outcome indicators. The term “contract” used here togenerically refer to a recurring revenue asset as discussed elsewhere inthis specification, and can include without limitation an existingcontract or agreement, a “free” or low cost warranty or service contract(e.g. as might be included with an original purchase of a product), orthe like. Expiring contracts can provide a basis for generation ofrecurring revenue renewal opportunities. A first indicator (e.g. ametric, designation, label, etc.) associated with an opportunity caninclude a resolution state. A resolution state is a designation orindicator regarding whether the opportunity remains “open” or,alternatively, has been “resolved” or “closed.” An “open” resolutionstate indicates that the opportunity has not yet been resolved. Theselling process has not ended in failure but instead continues to have achance of being resolved favorably.

A second indicator, metric, etc. associated with an opportunity is anoutcome state. An outcome state can be won or lost. As used herein, theterm “won” or “win” as it pertains to an outcome state refers to asuccessful renewal or establishment of paid service, for example asuccessful close of a renewal of a contract, other service arrangement,etc. based on an opportunity or group of opportunities. An outcome stateof “lost” or “no service” or the like refers to an outcome state inwhich no service was established for a given opportunity or group ofopportunities. For example, a lost opportunity based on an expiringcontract indicates that any revenue to be received by the commercialentity, as well as any service provided by the commercial entity insatisfaction of the contract of ends when the term of the existingagreement, service, arrangement, etc. expires, and no renewal orcontinuation of service is paid for by the customer.

A third indicator associated with an expiring contract or group ofexpiring contracts is a timing state, which relates to a currentresolution state of the opportunity relative to the date of expirationof the existing contract or other relationship underlying theopportunity. For example, an opportunity that has a resolution state ofopen on a date subsequent to the expiration date of an underlyingcontract, other service arrangement, etc. can have or be assigned a latetiming state. An opportunity that has a resolution state of “resolved”and that reached that state prior to the date of expiration of theunderlying contract, other service arrangement, etc. can have or beassigned an on-time or optionally an early timing state. Consistent withsome implementations of the current subject matter, a closed opportunitycan have or be assigned an early timing state if the date of closingoccurs prior to a sales period containing the expiration date of anunderlying contract, other service arrangement, etc. A closedopportunity can have or be assigned an on-time timing state if the dateof closing occurs in a same sales period containing the expiration dateof an underlying contract, other service arrangement, etc.

It can generally be observed that customer purchasing behavior isstrongly dependent on the timing with which a decision maker or othercontact at the customer is presented with an offer in relation to theexpiration date of an underlying contract, other service arrangement,etc. For example, if an opportunity for renewal is presented too early(e.g. one or more months in advance of the expiration date of theunderlying contract, other service arrangement, etc.), the urgency toclose may be lessened due to a tendency to prioritize decision pointsthat are more imminent. An opportunity that remains open after theexpiration date of the expiration date of the underlying contract, otherservice arrangement, etc. can also be at risk as the customer may growaccustomed to proceeding without the service. The urgency of renewing orresuming the service may be lessened, and such opportunities mayexperience a heightened risk of eventually resolving as a loss.

Consistent with implementations of the current subject matter, the aboveissues with timing of sales efforts in relation to an expiration date ofa contract, other service arrangement, etc. underlying an opportunitycan be addressed by grouping opportunities into three sets for a givensales period: carryover, current, and future opportunities. Carryoveropportunities can include contracts or other renewal opportunities thatexpired in a previous sales period but that were not resolved in thatprevious sales period and thus remain available to close in the currentsales period. As noted above, due to typical customer behavior, renewalcontracts tend to “age” quickly, meaning that contracts not renewed in arelatively short time after their expiration have a low probability ofever doing so. Current contracts are contracts that expire during thecurrent sales period. Typical customer behavior generally results inrenewal purchases occurring most frequently within about 90 days ofexpiration. As such, current contracts can be of prime importance tomany recurring revenue asset sales operations. Future contracts includethose contracts that expire after the end of the current sales period.Such contracts are available to be resolved during the current salesperiod. Closing of a contract early can be useful in protecting thevalue of the recurring revenue stream. However, closing contracts earlyat the expense of other contracts that may be expiring sooner can beless advantageous. It can be desirable to prioritize current andcarryover opportunities to prevent or reduce the occurrence of potentialgaps in the revenue stream.

FIG. 11 shows a diagram 1100 illustrating a overview of a guided salesprocess 1102 that includes a set of sales phases 1104, each includingone or more sales stages 1106. The sales phases can includequalification 1110, solution 1112, and close 1114. The sales stages caninclude not contacted 1116 and contacted 1120 within the qualificationsales phase 1110; quotation requested 1122A, quotation completed 1122B,and quote delivered 1124 within the solution sales phase 1112; andpurchase order received 1126A, customer commitment 1126B, and closedsale 1130 within the close sales phase 1114.

Implementations of the current subject matter can include at each salesphase 1104 process milestones (which can, for example define the salesstages within a sales phase), information capture requirements, andadvancement triggers that can assist in keeping the sale process ontrack. For example, the qualification sales phase 1110 can include oneor more process milestones defining the not contacted and contactedsales stages respectively based on whether an opportunity has been takenup by the sales staff and a qualified buyer (e.g. a decision maker) hasbeen reached. Information captured in the qualification sales phase 1110can optionally include one or more of logs of customer/qualified buyercontacts; notes, dates, contact type and method, etc. relating to thecontact events recorded in the process milestones, scheduling offollow-up tasks, etc. Advancement triggers for the qualification salesphase 1110 can include logging of a successful customer contact, whichcan cause the sales stage to move from not contacted 1116 to contact1120. In some examples, the state/phase and monitors can be managed fortriggering actions that cause state transitions. Triggering actions caninclude actions performed by a user in the context of an opportunity(e.g. clicking on “Log Customer Contact” and successfully executing) orcan include changes in any data in the opportunity that match apredefined pattern. The set of states, triggering actions, and statetransitions can be predefined as part of system configuration and can besubsequently modified.

For the solution sales phase 1112, process milestones can includedefinitions of the quote requested stage 1122A, quote completed salesstage 1122B, and the quote delivered stage 1124 as follows: a quote canbe registered as requested when a sales solution is reached (e.g. anagreement in principle regarding renewal of the opportunity is reachedbetween a sales representative and a decision maker at the customer); ascompleted when the quotation is ready for delivery; and as deliveredwhen it is actually delivered to the buyer, decision maker, etc.Information captured in the solution sales phase 1112 can optionallyinclude one or more of terms, service levels, and discounts requested bythe customer; a revisions history and notes; financial aspects of aquotation; a delivery date and target recipient of the quotation, etc.Advancement triggers for the solution sales phase 1112 can includedetection that a sales representative has requested a quotation (e.g.through functionality supported in a user interface of the recurringrevenue management system), that a sales operation team member haslogged a quotation as completed, that a sales operation team member haslogged a quotation as delivered, etc.

For the close sales phase 1114, process milestones can includedefinitions of the purchase order received sales stage 1126A, customercommitment sales stage 1126B, and closed sale sales stage 1130 asfollows: a customer commitment can be indicated by buyer commitment to apayment date, a purchase order received state can be indicated by apayment being sent for processing, and a closed sale can be indicated bya payment being fully processed. Information captured in the close salesphase 1114 can optionally include payment financial information, paymentand order dates, an order revision history, terms and service levels tobe applied to the renewed contract (e.g. if the terms and/or servicelevels have changed from a previous contract that has been renewed),etc. Advancement triggers for the close sales phase 1114 can include asales representative logging a customer commitment (e.g. to move theprocess to the customer commitment stage 1126A), a sales representativerequesting a booking (e.g. to move the process to the purchase orderreceived stage 1126B), a sales operation team member completing abooking (e.g. to move the process to the closed sale stage 1130), etc.

In the event that the sales process 1102 does not result in a successfulclose, the opportunity is classified as a “loss” as discussed above. Arecurring revenue management system 202 consistent with implementationsof the current subject matter can also include automated data collectionand processing related to losses. Such data and other information can behelpful in assessing the factors that resulted in an unsuccessful saleseffort for the opportunity and can support analysis of the efforts of asales team vs. well-defined metrics. Process milestones for a lostopportunity can include receiving an indication that the customer is notinterested in buying (e.g. a “no service” situation), that anopportunity should be identified as invalid (e.g. a “house account”),etc. Information captured for lost opportunities can includeidentification of the person or persons who declined the opportunity tobuy, one or more reasons given by that person or person or otherinformation acquired by the sales representative or other sale steammembers regarding reasons for the opportunity being lost, any notes bythe sales representative or other sales team member relating to orbacking up the loss, etc. Advancement triggers for a lost sale caninclude the sales representative resolving the opportunity in the systemas a loss.

As discussed briefly above, an asset data model 300 consistent withimplementations of the current subject matter can track reasons,explanations, etc. relating to resolution of an opportunity, group ofopportunities, a single offer, etc. (e.g. a “why” regarding a win orloss outcome for a given opportunity or offer). Outcomes can include aclosed (successful) sale, a “house” account (e.g. an invalidopportunity, perhaps relating to an underlying product or service that acustomer once had but has since disposed or), or a “no service” outcomein which the customer simply chooses not to purchase a service agreementor subscription or allows an existing agreement to expire withoutrenewing. One or more reason codes can be included and tracked as partof the asset data model for each type of outcome. These codes can beuseful in providing feedback to service salespersons, and as additionalinput data for further analytics and business intelligence processes.For a closed sale, an outcome code can include, without limitation, abackdated sale, a long co-term sale, a short co-term sale, a discountedsale, a multi-year advance sale, a pricing change sale, are-certification fee included sale, a renewed at par sale, a servicedowngrade sale, a service upgrade sale, an uncovered sale, or the like.For a house account outcome, an outcome code can indicate, withoutlimitation, one or more of a bad data situation, a cancelled contract oragreement, a covered service, a duplicate service, an end of supportcondition, an evergreen billing situation, an international transaction,a leasing situation, an OEM customer, an “other” explanation, a productreturn, a sales pullback by the customer, or the like. For a no serviceoutcome, an outcome code can indicate, without limitation, one or moreof a replacement of the product by the customer, a competitivedistributed value added reseller (DVAR), a competitive productreplacement, another explanation for a competitive service loss, acustomer cost-benefit decision, that the customer no longer exists, acustomer satisfaction driven refusal to buy or renew, that the coveredproduct or service is at the end of its service life, some other datamanagement issue, a decommissioned or otherwise removed from serviceproduct, a third party maintenance agreement, an unresponsive end user,an unresponsive value added reseller (VAR), a no service VAR, or thelike.

Descriptions herein of a sales process and sales personnel makereference in some examples to sales representatives and sale operationteam members. One aspect of the current subject matter can include anintegrated sales operations approach in which activities relating to theselling process for renewals between can be bifurcated between sellingactivity and back office sales support activity. Selling activity inthis example refers to what is typically assumed to be an importantsales representative skill, specifically contacting buyers, decisionmakers, etc. at a customer and presenting an attractive valueproposition that motivates the purchasing party to choose to complete apurchase. The skill set relating to such activities is a valuable one toa commercial entity, so a recurring revenue management approach thatmaximizes the utilization of these skills can be quite advantageous.Accordingly, user interface views provided by a recurring revenuemanagement system 202 consistent with implementations of the currentsubject matter can support different view for different users.

FIG. 12 shows an annotated screenshot view 1200 that indicates divisionof roles among sales representatives 412, sales operations team members420, and sales managers 1202. As shown in the example screenshot view1300 of FIG. 13, a sale representative 412 can be shown a focused viewthat supports one or more of opportunity research, contacting ofcustomers, creating quotation and booking requests, pipeline management,and the like. As shown in the example screenshot view 1400 of FIG. 14, asales operations team member 420 can be shown a focused view thatsupports one or more of completing quotation and booking requests; datalook-up relating to opportunities, assets, and the like; service-levelagreements (SLA) and quality management; and the like. As shown in theexample screenshot view 1500 of FIG. 15, a sales manager 1202 can beshown a focused view that supports one or more of sales and salesoperation oversight, manager level permission for escalations, taskingopportunity assignments, target and forecast management, and the like.

Through the use of a recurring revenue management system 202 thatsupports these focused views on the user interface that are tailored tospecific roles in a sales team, the recurring revenue management system202 enables tight coupling between quotation generation, bookinggeneration, etc. in response to requests from the sales representative412 that are generated through buyer contacts and sales activities.Additionally, the underlying asset data model 300, which can be part ofa recurring revenue management system 202 consistent with implicationsof the current subject matter, can support tight coupling of the salesprocess with underlying data services. In this manner, data managementand quality of data underlying the sales process and the renewal processcan be optimized. Furthermore, data that are quality enhanced or addedto as part of a sales process can optionally be propagated back to oneor more external client systems 114.

Management, measurement, and monitoring of service sales activities canbe supported by one or more implementations of the current subjectmatter to ensure a consistent approach to managing opportunities, quotesand bookings activities. A recurring revenue management system caninclude one or more features such as detailed reports that can enable asales staff member (e.g. a sales representative) to proactively identifyand address at-risk opportunities, pipeline dashboards to enable amanager to view pacing by time period and production metrics,opportunity details to enable viewing of deal progress and loggedactivities, territory management to enable manager and the like to drivesales staff behavior by assigning tasks to opportunities based onexisting priorities, and the like. FIG. 16 shows an example screenshotview 1600 illustrating features of an analytics view displaying metricsrelating to closed opportunity value. FIG. 17 shows an examplescreenshot view 1700 illustrating features of an analytics viewdisplaying metrics relating to a no service (e.g. “loss”) rate forclosed opportunities. FIG. 18 shows an example screenshot view 1800illustrating features of an analytics view displaying a forecastdashboard for displaying forecast information. FIG. 19 shows an examplescreenshot view 1900 illustrating features of an analytics viewdisplaying metrics relating to an opportunity pipeline.

A recurring revenue management approach consistent with one or moreimplementations of the current subject matter can generate dynamicfeedback and directed task navigation for a user of a recurring revenuemanagement system based on one or more frameworks and roles as notedabove. In some examples, the directed task navigation can be designed tomaximize the time that sales representatives can spend on sales-relatedactivities for which their skill-set is an advantageous match. Based onthe ability to identify sales issues or operational issues discovered ina role-based dashboard by using or identifying data via search facets, auser or manager can logically group a set of opportunities that meetcertain characteristics. For example, a user or manager can readily findall opportunities that have an expiration date of prior to a target dateor date range (e.g. prior to 2014) and a sales stage of Not Contactedwhere the Incumbent Reseller is a specific company (e.g. ABC, Inc.).Those opportunities can then be assigned to a specific task type, suchas creating a quote, requesting a booking, contacting the customer, orresolving data issues and assigned to a specific role or user.Progression or evolution of the set of activities relative to thatlogical grouping of work can then be monitored over time as activitiesare completed or resolved.

FIG. 13 shows an annotated screenshot view 1300 of a user interfaceillustrating features that can be included consistent withimplementations of the current subject matter. As shown, the userinterface can include multiple sections, which can optionally beaccessed via a tab bar or other user interface functionality and neednot be all displayed on a same screen. The multiple sections can berelated to various components of the sales process, for example thosediscussed in relation to FIG. 4 above. For example, a first section ofthe screen 1302 can provide one or more pieces of information relatingto a covered asset 404. Another section of the screen 1304 can provideone more piece of information relating to a service asset 402, oralternatively, more than one service asset 402, that are related to thecovered asset 404. The service asset section 1304 can includeinformation relating to an offer 406 that is related to the serviceasset 402, as well as information relating to an opportunity thatincludes the offer 406. Via a tab bar 1306, or other user interfacefunctionality, it can be possible for a user to link to additionalscreens that provide more detailed information on aspects of thecustomer relationship including characterization of the customersinstall base or other underlying product or contract information 1306,current or past year sales information 1310, transaction information1312, or the like.

FIG. 14 shows a diagram illustrating a process consistent withimplementations of the current subject matter for generating offersbased on existing service assets 402 and the underlying covered assets404. Using the relationships between the data objects 301 in the assetdata model 300, links can be identified and exploited between coveredassets 404 that are part of an installed product base at a customer andrecurring revenue assets (service assets 402). A service asset 402generally has a current term 1402, which further includes an expirationdate. A service asset 402 also generally has a current service level1404 associated with it as well as a current contract value 1406. Thesedata are generally included within an instance of an asset data object304 that is part of the asset data model 300, that represents theservice asset 402, and that is linked by a relationship with theinstance of a product data object 302 representing the covered asset404. As the expiration date of the term 1402 of the service asset 402approaches, and offer 406 is generated. The offer 406 includes an offerterm 1410, and offer service level 1412, and a offer target amount 1414.The offer term 1410 indicates the proposed period of time for which theservice asset 402 is to be renewed. The offer service level 1412 isgenerally initially set at the current service level 1404, and the offertarget amount 1414 is generally initially set at the current contractvalue 1406.

A recurring revenue management system 202 consistent withimplementations of the current subject matter can employ an asset datamodel specialized for recurring revenue asset sales and renewals. Theasset data model can include fields related to service timing events,service outcomes, relationships to other assets and other contextspecific fields. For example, in support of new sales of recurringrevenue assets (e.g. a new service contract, a new subscription, etc.),a recurring revenue management system can process information receivedfrom the external client sources and optionally also updated informationgenerated through sales contacts, ongoing sales and renewal operations,and other functions supported by the recurring revenue management systemto populate data values in one or more fields relating to one or more ofa local opportunity amount, a local transaction amount, an earliestexisting end date, an earliest new start date, a latest new end date, acommit level (Red, Yellow, Black, Green, or other representationalscheme), a renewal sales stage, a covered product, a service product, aservice product description, a client batch quarter, a win/loss resultexplanation, an existing contract number, an existing purchase ordernumber, an existing distributor, an existing reseller, a newdistributor, a new reseller, and the like.

In support of renewal sales of recurring revenue assets (e.g. a renewalof an existing contract, a value-added upsell of an existing contract,etc.), a recurring revenue management system can process informationreceived from the external client sources and optionally also updatedinformation generated through sales contacts, ongoing sales and renewaloperations, and other functions supported by the recurring revenuemanagement system to populate data values in one or more fields relatingto one or more of a not contacted/contacted status, a quote requestedstatus, a quote delivered status, a customer commitment status, apurchase order received status, as closed sale status, etc.

A recurring revenue management system consistent with implementations ofthe current subject matter can include a mechanism for coordinatingonline and offline activities of sales teams and individuals tooptimally coincide with expiration or other timing events associatedwith a service sale. Such mechanisms can include automated workflowsthat guide when and how customers are contacted, when activities such asquote request and quote presentment are executed, and when escalationsoccur. The sales process can incorporate one or more key metricsassociated with service sales, such as for example resolution rate,close rate, conversion rate, and the like that can be used to optimizeperformance.

A recurring revenue management system consistent with implementations ofthe current subject matter can also implement one or more teamcoordination processes for pipelines and territories that ensureoptimization of team outcomes. The processes, metrics and workflowsrequired to drive a sales outcome can be encoded in software andcomputer program products, to enable the recurring revenue managementsystem to perform predictive performance management.

Automated or semi-automated forecasting of team-based service sales canalso be supported by a recurring revenue management system consistentwith implementations of the current subject matter. The recurringrevenue management system can provide a framework for data collectionfrom representatives based on key sales metrics, and which allowsrepresentatives to follow a periodic (i.e. weekly) computer-generateddiscipline process, leading to accurate monthly and quarterly salesforecasts. The forecasting processes can incorporate a dual estimationapproach, where bottom-up (individual opportunity assessment-based) andtop-down (expiration date grouping-based) calculations are employed.

User-configured sales automation programs can be used to drivespecialized process behavior for specific sets of opportunities andservice sales operations. These automation programs can include adaptivequote and bookings management for service sales. Capabilities ofadaptive quote and bookings management functionality consistent with oneor more implementations of the current subject matter can include quoterequest categorization (e.g. to identify key properties of the quote toguide processing), quote request routing (e.g. to route a request to aproper resource, either human or automated, to maximize processingefficiency), statistical prediction of quote request outcomes (e.g. topredict a probability of rejection and project a required processingtime), SLA management (e.g. to effectively measure on-time performanceand to guide expectations of quote requestors), throughput forecastingand management (e.g. to manage workload and properly price quoteprocessing services), semi-automated bookings reconciliation (e.g. toallow reconciling an opportunity-based sale, such as from a CRMperspective, with an external order management system view), a dynamic(i.e. rules-based or algorithmic) performance management engine foroptimizing work/activity allocation, and the like.

A recurring revenue management system can further include modules formanaging a complex channel partner network. The capabilities of thechannel partner network management module(s) can include one or more ofa federated permission model that allows OEMs to efficiently distributeaccess to opportunities across a multi-level network of channelpartners, incumbency logic that determines which opportunities should bevisible to any given channel partner, gathering and disseminatingforecast information from each channel, sales incentive programs thatcan be executed across the channel, self-service management by channelpartners (e.g. to allow management of users and permissions for thatpartner), quote processing management (e.g. dynamically across amulti-level channel), and the like. For example, a user interface can beprovided for access by channel partners to display a selection ofrenewal ready defined opportunities for these channel partners to pursuebased on underlying covered assets sold, leased, etc. by an originalequipment manufacturer (OEM). This or other user interfaces can includeone or more renewal metrics similar to those discussed above, and can bebased on value to the OEM. In addition, data regarding renewal processoperations occurring at a channel partner can optionally be synchronizedback to an asset data model 300 maintained by or on behalf of the OEM.Projections, targeting, forecasting, and the like as discussed above canbe supported for a channel partner, for example in a manner similar tothose approaches discussed elsewhere herein.

A recurring revenue management system can also support holistic analysisof customer success management (CSM), which can include one or more ofservice asset analysis, sales execution analysis, sales operationsanalysis, and the like. These features of SRM can include analyticalcapabilities to collect and display benchmark comparisons for allaspects of SRM.

As an example of these benchmarking capabilities, the recurring revenuemanagement system 202 can access aggregate information regarding servicerevenue and other recurring revenue stream renewals, sales, etc.generated by one or more other commercial entities external to thecommercial entity whose sales processes are automated through use of therecurring revenue management system 202. For example, if the recurringrevenue management system 2902 is provided via a software as a service(SaaS) framework, the SaaS provider can have the ability to aggregate,e.g. into a database, the sales process outcome data from a number ofcommercial entities and to make these aggregated data available in theform of benchmarks and other metrics against which the sales outcomes ofa specific commercial entity, a sales team within the commercial entity,an individual sales representative, etc. can readily be compared.

As a further feature of a recurring revenue management system 202consistent with some implementations of the current subject matter, theaggregated data discussed above can serve as an input for one or morepredictive models that can be applied in improving the forecasting andtargeting functions discussed above. In one example, a set of parametersrepresentative of a specific offer can be analyzed by a predictive modelbased on outcomes of other offers, either within a given commercialentity or across aggregated data generated by more than one commercialentity to calculate a predicted outcome for that offer. The results ofthis kind of modeling can optionally be used in an iterative manner, forexample to test different possible sales approaches (e.g. timing of afirst contact relative to a current service asset expiration date, timeelapsed in the sales process from first contact by a salesrepresentative to delivery of a quote, etc.) to identify a set ofpredicted best practices.

Predictive models can also support aggregate opportunity outcomepredictions, for example to forecast renewal rates, values, resolutionrates, etc. for a group of available offers or opportunities. Theoutcome predictions can optionally include projections of the value of aset of service assets at some point in the future, a total amount ofprojected revenue for a set of service assets, etc.

Predictive models based on historical sales execution data can be usedto provide one or more of the following types of predictive analytics:predictions of a probability of an offer closing within a particulartime window based on customer, product, and time gap from expiration;predictions of an expected value of an offer if closed within aparticular time window based on customer, product, and time gap fromexpiration; predictions of a probability of an upgrade of an offer ifthe offer is closed within a particular time window based on customer,product, and time gap from expiration; etc. Predictive models consistentwith implementations of the current subject matter can be built usingmachine learning approaches such as survival analysis, clustering, andregression analysis. The models can be encoded into the application foruse in the form of predictor functions that augment data in the system.

FIG. 2000 shows a diagram of a computing landscape consistent with oneor more implementations of the current subject matter. A computingsystem 2002 can perform operations, execute code, or otherwise implementa recurring revenue management system 202. The recurring revenuemanagement system 202, which can include a asset data model 300,provides one or more features as discussed above. The computing system2002 can be accessed by one or more remote machines 2008, for examplevia a direct connection, a local terminal, or over a network 2010 (e.g.a local area network, a wide area network, a wireless network, theInternet, or the like). External client systems 114 can also be accessvia direct connections and/or over the same network 2010 or one or moreother networks. The asset data model 300 and recurring revenuemanagement system 202 can be in contact with one or more repositories2016, again either via direct connection and/or over the same network2010 or one or more other networks. Aspects of the current subjectmatter can also be provided via a multi-tenant environment, in whichdata records, etc. of multiple client organizations are retained on acommon repository or set of repositories while being isolated fromaccess except by the owner of the specific data records, etc.

One or more aspects or features of the subject matter described hereincan be realized in digital electronic circuitry, integrated circuitry,specially designed application specific integrated circuits (ASICs),field programmable gate arrays (FPGAs) computer hardware, firmware,software, and/or combinations thereof. For example, any references tomethods, algorithms, calculations, receiving data, sending data, storingdata, other data processing tasks or the like can include the executionof such operations at least in part by at least system, each of whichincludes at least one programmable processor. These various aspects orfeatures can include implementation in one or more computer programsthat are executable and/or interpretable on a programmable systemincluding at least one programmable processor, which can be special orgeneral purpose, coupled to receive data and instructions from, and totransmit data and instructions to, a storage system, at least one inputdevice, and at least one output device. The programmable system orcomputing system may include clients and servers. A client and serverare generally remote from each other and typically interact through acommunication network. The relationship of client and server arises byvirtue of computer programs running on the respective computers andhaving a client-server relationship to each other.

These computer programs, which can also be referred to as programs,software, software applications, applications, components, or code,include machine instructions for a programmable processor, and can beimplemented in a high-level procedural and/or object-orientedprogramming language, and/or in assembly/machine language. As usedherein, the term “machine-readable medium” refers to any computerprogram product, apparatus and/or device, such as for example magneticdiscs, optical disks, memory, and Programmable Logic Devices (PLDs),used to provide machine instructions and/or data to a programmableprocessor, including a machine-readable medium that receives machineinstructions as a machine-readable signal. The term “machine-readablesignal” refers to any signal used to provide machine instructions and/ordata to a programmable processor. The machine-readable medium can storesuch machine instructions non-transitorily, such as for example as woulda non-transient solid-state memory or a magnetic hard drive or anyequivalent storage medium. The machine-readable medium can alternativelyor additionally store such machine instructions in a transient manner,such as for example as would a processor cache or other random accessmemory associated with one or more physical processor cores.

To provide for interaction with a user, one or more aspects or featuresof the subject matter described herein can be implemented on a computerhaving a display device, such as for example, a liquid crystal display(LCD), or a light emitting diode (LED) monitor for displayinginformation to the user and a keyboard and a pointing device, such asfor example a mouse or a trackball, by which the user may provide inputto the computer. Other kinds of devices can be used to provide forinteraction with a user as well. For example, feedback provided to theuser can be any form of sensory feedback, such as for example visualfeedback, auditory feedback, or tactile feedback; and input from theuser may be received in any form, including, but not limited to,acoustic, speech, or tactile input. Other possible input devicesinclude, but are not limited to, touch screens or other touch-sensitivedevices such as single or multi-point resistive or capacitive trackpads,voice recognition hardware and software, optical scanners, opticalpointers, digital image capture devices and associated interpretationsoftware, and the like.

The subject matter described herein can be embodied in systems,apparatus, methods, and/or articles depending on the desiredconfiguration. The implementations set forth in the foregoingdescription do not represent all implementations consistent with thesubject matter described herein. Instead, they are merely some examplesconsistent with aspects related to the described subject matter.Although a few variations have been described in detail above, othermodifications or additions are possible. In particular, further featuresand/or variations can be provided in addition to those set forth herein.For example, the implementations described above can be directed tovarious combinations and subcombinations of the disclosed featuresand/or combinations and subcombinations of several further featuresdisclosed above. In addition, the logic flows depicted in theaccompanying figures and/or described herein do not necessarily requirethe particular order shown, or sequential order, to achieve desirableresults. Other implementations may be within the scope of the followingclaims.

1-27. (canceled)
 28. A computer program product comprising amachine-readable medium storing instructions that, when executed by atleast one programmable processor of a recurring revenue managementserver, cause the at least one programmable processor to performoperations comprising: generating a user interface for an electronicdisplay, the user interface including a graphical representation of oneor more service asset data objects generated during a pre-defined timewindow, the graphical representation representing the one or moreservice asset data objects and displaying one or more metrics thatdefine the one or more service asset data objects; assigning a datastructure to information contained in records of the service asset dataobjects received from an owner of the service asset data objects, thedata structure is configured to maintain provenance data and relationaldata of the information contained in the records of the service assetdata objects to facilitate use of the provenance data and relationaldata by the recurring revenue management server and to facilitateintegration of additional information from the owner of the serviceasset data objects; maintaining records of the service asset dataobjects at the recurring revenue management server, the recordsincluding contract information associated with the service asset dataobjects and a set of offers for renewal of the service asset dataobjects; synchronizing additional information received from the owner ofthe service asset data objects with the information assigned to the datastructure, the synchronizing maintaining the provenance data andrelational data of the information to integrate the additionalinformation; differentiating the set of offers for renewal of theservice assets within a sales period; analyzing a set of parametersrepresentative of the one or more offers of the set of offers, the setof parameters comprising at least one user-provided constraint on anexpected outcome of the one or more offers of the set of offers;calculating a predicted outcome for the set of offers for a beginningportion of the sales period, the predicted outcome calculated based onan applied predictive model using the set of parameters and thesynchronized additional formation as inputs; recalculating the predictedoutcome for the set of offers for an end portion of the sales periodbased on an updated set of parameters comprising an updated constraintand the predicted outcome for the set of offers for the beginningportion of the sales period, the updated constraint reflecting at leastone sales outcome; and generating a set of finalized offers andconcurrently displaying the set of finalized offers in the userinterface with a graphical representation of the predicted outcome forthe set of finalized offers, the set of finalized offers generated basedon the updated predicted outcome for the set of offers, wherein the setof finalized offers includes offers having a recalculated predictedoutcome indicative of a predefined level of success.
 29. The computerprogram product of claim 28, wherein the differentiating is based atleast in part on an expiration date of a service asset associated withthe one or more offers of the set of offers.
 30. The computer programproduct of claim 28, wherein the predictive model comprisesdeterminations based on outcomes of other offers for renewal of theservice assets.
 31. The computer program product of claim 28, whereinthe other offers comprise at least one of other offers within a givencommercial entity or other offers across aggregated data generated bymore than one commercial entity.
 32. The computer program product ofclaim 28, wherein the operations further comprise performing thecalculating a plurality of times to test different possible salesapproaches by altering the set of parameters and iterating to identify aset of best practices based on historical sales data.
 33. The computerprogram product of claim 28, wherein the set of parameters comprise atleast one of a timing of a first contact by a sales representative witha customer relative to a current service asset expiration date, and atime elapsed in a sales process from the first contact by a salesrepresentative to delivery of a quote.
 34. The computer program productof claim 28, wherein the at least one user-provided constraint isprovided by a user providing input to a user device.
 35. A systemcomprising: at least one programmable processor; and a machine-readablemedium storing instructions that, when executed by the at least oneprogrammable processor of a recurring revenue management server, causethe at least one programmable processor to perform operationscomprising: generating a user interface for an electronic display, theuser interface including a graphical representation of one or moreservice asset data objects generated during a pre-defined time window,the graphical representation representing the one or more service assetdata objects and displaying one or more metrics that define the one ormore service asset data objects; assigning a data structure toinformation contained in records of the service asset data objectsreceived from an owner of the service asset data objects, the datastructure is configured to maintain provenance data and relational dataof the information contained in the records of the service asset dataobjects to facilitate use of the provenance data and relational data bythe recurring revenue management server and to facilitate integration ofadditional information from the owner of the service asset data objects;maintaining records of the service asset data objects at the recurringrevenue management server, the records including contract informationassociated with the service asset data objects and a set of offers forrenewal of the service asset data objects; synchronizing additionalinformation received from the owner of the service asset data objectswith the information assigned to the data structure, the synchronizingmaintaining the provenance data and relational data of the informationto integrate the additional information; differentiating the set ofoffers for renewal of the service assets within a sales period;analyzing a set of parameters representative of the one or more offersof the set of offers, the set of parameters comprising at least oneuser-provided constraint on an expected outcome of the one or moreoffers of the set of offers; calculating a predicted outcome for the setof offers for a beginning portion of the sales period, the predictedoutcome calculated based on an applied predictive model using the set ofparameters and the synchronized additional formation as inputs;recalculating the predicted outcome for the set of offers for an endportion of the sales period based on an updated set of parameterscomprising an updated constraint and the predicted outcome for the setof offers for the beginning portion of the sales period, the updatedconstraint reflecting at least one sales outcome; and generating a setof finalized offers and concurrently displaying the set of finalizedoffers in the user interface with a graphical representation of thepredicted outcome for the set of finalized offers, the set of finalizedoffers generated based on the updated predicted outcome for the set ofoffers, wherein the set of finalized offers includes offers having arecalculated predicted outcome indicative of a predefined level ofsuccess.
 36. The system of claim 35, wherein the differentiating isbased at least in part on an expiration date of the service assetassociated with the one or more offers of the set of offers.
 37. Thesystem of claim 35, wherein the predictive model comprisesdeterminations based on outcomes of other offers for renewal of serviceassets.
 38. The system of claim 35, wherein the other offers comprise atleast one of other offers within a given commercial entity or otheroffers across aggregated data generated by more than one commercialentity.
 39. The system of claim 35, wherein the operations furthercomprise performing the calculating a plurality of times to testdifferent possible sales approaches by altering the set of parametersand iterating to identify a set of best practices based on historicalsales data.
 40. The system of claim 35, wherein the set of parameterscomprise at least one of a timing of a first contact by a salesrepresentative with a customer relative to a current service assetexpiration date, and a time elapsed in a sales process from the firstcontact by a sales representative to delivery of a quote.
 41. Acomputer-implemented method for implementation by one or more dataprocessors forming part of a recurring revenue management server, themethod comprising: generating a user interface for an electronicdisplay, the user interface including a graphical representation of oneor more service asset data objects generated during a pre-defined timewindow, the graphical representation representing the one or moreservice asset data objects and displaying one or more metrics thatdefine the one or more service asset data objects; assigning a datastructure to information contained in records of the service asset dataobjects received from an owner of the service asset data objects, thedata structure is configured to maintain provenance data and relationaldata of the information contained in the records of the service assetdata objects to facilitate use of the provenance data and relationaldata by the recurring revenue management server and to facilitateintegration of additional information from the owner of the serviceasset data objects; maintaining records of the service asset dataobjects at the recurring revenue management server, the recordsincluding contract information associated with the service asset dataobjects and a set of offers for renewal of the service asset dataobjects; synchronizing additional information received from the owner ofthe service asset data objects with the information assigned to the datastructure, the synchronizing maintaining the provenance data andrelational data of the information to integrate the additionalinformation; differentiating the set of offers for renewal of theservice assets within a sales period; analyzing a set of parametersrepresentative of the one or more offers of the set of offers, the setof parameters comprising at least one user-provided constraint on anexpected outcome of the one or more offers of the set of offers;calculating a predicted outcome for the set of offers for a beginningportion of the sales period, the predicted outcome calculated based onan applied predictive model using the set of parameters and thesynchronized additional formation as inputs; recalculating the predictedoutcome for the set of offers for an end portion of the sales periodbased on an updated set of parameters comprising an updated constraintand the predicted outcome for the set of offers for the beginningportion of the sales period, the updated constraint reflecting at leastone sales outcome; and generating a set of finalized offers andconcurrently displaying the set of finalized offers in the userinterface with a graphical representation of the predicted outcome forthe set of finalized offers, the set of finalized offers generated basedon the updated predicted outcome for the set of offers, wherein the setof finalized offers includes offers having a recalculated predictedoutcome indicative of a predefined level of success.
 42. Thecomputer-implemented method of claim 41, wherein the differentiating isbased at least in part on an expiration date of the service assetassociated with the one or more offers of the set of offers.
 43. Thecomputer-implemented method of claim 41, wherein the predictive modelcomprises determinations based on outcomes of other offers for renewalof service assets.
 44. The computer-implemented method of claim 41,wherein the other offers comprise at least one of other offers within agiven commercial entity or other offers across aggregated data generatedby more than one commercial entity.
 45. The computer-implemented methodof claim 41, wherein the operations further comprise performing thecalculating a plurality of times to test different possible salesapproaches by altering the set of parameters and iterating to identify aset of best practices based on historical sales data.
 46. Thecomputer-implemented method of claim 41, wherein the set of parameterscomprise at least one of a timing of a first contact by a salesrepresentative with a customer relative to a current service assetexpiration date, and a time elapsed in a sales process from the firstcontact by a sales representative to delivery of a quote.